DeepSTA: A Spatial-Temporal Attention Network for Logistics Delivery Timely Rate Prediction in Anomaly Conditions.

@inproceedings{yi-2023-deepsta, author = {Jinhui Yi and Huan Yan and Haotian Wang and Jian Yuan and Yong Li}, editor = {Ingo Frommholz and Frank Hopfgartner and Mark Lee and Michael P. Oakes and Mounia Lalmas-Roelleke and Min Zhang and Rodrygo L. T. Santos}, title = {{DeepSTA: A Spatial-Temporal Attention Network for Logistics Delivery Timely Rate Prediction in Anomaly Conditions}}, booktitle = {{Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023}}, pages = {4916--4922}, publisher = {ACM}, year = {2023}, url = {https://doi.org/10.1145/3583780.3614671}, doi = {https://doi.org/10.1145/3583780.3614671} }